Plots - artificial vs. natural experiment
Lisa Schunk
2022-10-06 17:23:47
1 Goal of the script
This script plots all variables to see which ones should be used for further analysis.
Scatterplot of each variable will be plotted.
dir_in <- "analysis_all.cycles/derived_data/"
dir_out <- "analysis_all.cycles/plots"Raw data must be located in ~/analysis_all.cycles/derived_data/.
Formatted data will be saved in ~/analysis_all.cycles/plots.
1.1 The knit directory for this script is the project directory.
2 Load packages
pack_to_load <- c("R.utils", "tools", "ggplot2", "doBy", "tidyverse", "patchwork", "ggsci")
sapply(pack_to_load, library, character.only = TRUE, logical.return = TRUE)Warning: package 'R.utils' was built under R version 4.1.3Warning: package 'doBy' was built under R version 4.1.3Warning: package 'tibble' was built under R version 4.1.3Warning: package 'tidyr' was built under R version 4.1.3Warning: package 'readr' was built under R version 4.1.3Warning: package 'dplyr' was built under R version 4.1.3 R.utils tools ggplot2 doBy tidyverse patchwork ggsci
TRUE TRUE TRUE TRUE TRUE TRUE TRUE 3 Get name, path and information of the file
data_file <- list.files(dir_in, pattern = "\\.Rbin$", full.names = TRUE)
md5_in <- md5sum(data_file)
info_in <- data.frame(file = basename(names(md5_in)), checksum = md5_in, row.names = NULL)4 Load data into R object
imp_data <- loadObject(data_file)
str(imp_data)'data.frame': 120 obs. of 43 variables:
$ Sample : chr "FLT4-12" "FLT4-12" "FLT4-12" "FLT4-12" ...
$ Cycle : Factor w/ 5 levels "0","50","250",..: 1 1 1 4 4 4 5 5 5 3 ...
$ Spot : chr "A" "B" "C" "A" ...
$ Raw.material : Factor w/ 2 levels "flint","lydite": 1 1 1 1 1 1 1 1 1 1 ...
$ Contact.material : chr "skin pad" "skin pad" "skin pad" "skin pad" ...
$ Acquisition.date.time : chr "8/10/2021 7:41:36 AM" "8/10/2021 7:59:13 AM" "8/10/2021 8:14:18 AM" "10/12/2021 8:36:04 AM" ...
$ Analysis.date : chr "2022/10/04" "2022/10/04" "2022/10/04" "2022/10/04" ...
$ Analysis.time : 'times' num 0.664 0.665 0.665 0.665 0.665 ...
..- attr(*, "format")= chr "h:m:s"
$ Sq : num 863 1315 809 1812 941 ...
$ Ssk : num 2.051 2.239 1.861 2.288 0.571 ...
$ Sku : num 9.09 11.98 10.14 9.83 3.4 ...
$ Sp : num 4894 7904 4254 10948 3385 ...
$ Sv : num 1498 3242 2077 2361 2514 ...
$ Sz : num 6392 11146 6331 13309 5899 ...
$ Sa : num 605 813 515 1215 714 ...
$ Smr : num 0.668 0.4112 1.4301 0.0768 1.2472 ...
$ Smc : num 997 1053 610 2112 1377 ...
$ Sxp : num 838 1869 1297 1533 1463 ...
$ Sal : num 13.1 11 17.5 12.8 15 ...
$ Str : num 0.212 NA 0.749 0.151 0.758 ...
$ Std : num 3.49 130.02 115.75 130 98.74 ...
$ Sdq : num 0.232 0.286 0.153 0.511 0.319 ...
$ Sdr : num 2.33 3.36 1.11 9.22 4.5 ...
$ Vm : num 0.0991 0.1868 0.1072 0.2229 0.0532 ...
$ Vv : num 1.096 1.24 0.717 2.335 1.43 ...
$ Vmp : num 0.0991 0.1868 0.1072 0.2229 0.0532 ...
$ Vmc : num 0.559 0.714 0.508 1.136 0.768 ...
$ Vvc : num 1.055 1.127 0.641 2.264 1.333 ...
$ Vvv : num 0.0415 0.1124 0.076 0.0704 0.0969 ...
$ Maximum.depth.of.furrows: num 2834 3768 2058 6620 3031 ...
$ Mean.depth.of.furrows : num 1250 1154 683 2720 1205 ...
$ Mean.density.of.furrows : num 2801 3123 3305 3697 4268 ...
$ Isotropy : num 21.2 NA 74.9 15.1 75.8 ...
$ First.direction : num 1.71e-03 1.27e+02 1.29e+02 1.37e+02 1.31e-02 ...
$ Second.direction : num 137 117 112 118 147 ...
$ Third.direction : num 154.8 90 105 154.8 98.7 ...
$ Texture.isotropy : num 47 13.8 80.3 10.5 85.8 ...
$ epLsar : num 0.005379 0.004747 0.000594 0.006092 0.002104 ...
$ NewEplsar : num 0.0178 0.0163 0.0176 0.0177 0.0172 ...
$ Asfc : num 3.94 5.89 1.79 14.2 7.9 ...
$ Smfc : num 14.89 58.74 4.31 3.7 1.75 ...
$ HAsfc9 : num 0.649 8.295 0.6 0.703 0.302 ...
$ HAsfc81 : num 1.885 12.216 1.174 1.652 0.594 ...
- attr(*, "comment")= Named chr [1:42] "µm" "points" "µm" "µm" ...
..- attr(*, "names")= chr [1:42] "Axis length - X" "Axis size - X" "Axis spacing - X" "Axis length - Y" ...# add another column which concatenates Sample and Spot
imp_data$ID <- paste(imp_data$Sample, imp_data$Spot)
imp_data <- imp_data[c(1:3, 44, 4:43)]
# copy the column Cycle and convert into numeric
imp_data$Stroke <- paste(imp_data$Cycle)
imp_data[["Stroke"]] <- as.numeric(imp_data[["Stroke"]],levels=c("0", "50", "250", "1000",
"2000"))The imported file is: “~/analysis_all.cycles/derived_data/AvsN_all.cycles.Rbin”
5 Prepare variables
5.1 Define numeric variables
num.var <- 10:44The following variables will be used:
[10] Sq
[11] Ssk
[12] Sku
[13] Sp
[14] Sv
[15] Sz
[16] Sa
[17] Smr
[18] Smc
[19] Sxp
[20] Sal
[21] Str
[22] Std
[23] Sdq
[24] Sdr
[25] Vm
[26] Vv
[27] Vmp
[28] Vmc
[29] Vvc
[30] Vvv
[31] Maximum.depth.of.furrows
[32] Mean.depth.of.furrows
[33] Mean.density.of.furrows
[34] Isotropy
[35] First.direction
[36] Second.direction
[37] Third.direction
[38] Texture.isotropy
[39] epLsar
[40] NewEplsar
[41] Asfc
[42] Smfc
[43] HAsfc9
[44] HAsfc816 Plot each of the selected numeric variable
6.1 Sorted by contact material
6.1.1 artificial vs. natural bone: flint samples
flint <- filter(imp_data, Raw.material == "flint")
for (i in num.var){
# get the min/max range of the data set
range_var <- range(flint[i])
# plot bone plate
f_bp <- ggplot(flint[grep("bone plate", flint[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "flint - bone plate", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
geom_line(aes(group = Spot,), colour = "#FAD510") +
scale_colour_manual(values = "#FAD510") +
theme_classic()
# plot cow scapula
f_cs <- ggplot(flint[grep("cow scapula", flint[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "flint - cow scapula", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
scale_colour_manual(values = "#CB2314") +
geom_line(aes(group = Spot,), colour = "#CB2314") +
theme_classic()
# combine the flint plots
f.bone <- f_bp + f_cs + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(f.bone)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_f.bone_", names(imp_data)[i],
".pdf")
ggsave(filename = file_out, plot = f.bone, path = dir_out, device = "pdf")
}
Warning: Removed 4 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 1 rows containing missing values (geom_point).Warning: Removed 1 row(s) containing missing values (geom_path).Warning: Removed 4 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 1 rows containing missing values (geom_point).Warning: Removed 1 row(s) containing missing values (geom_path).
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Warning: Removed 1 rows containing missing values (geom_point).Warning: Removed 1 rows containing missing values (geom_point).
6.1.2 artificial vs. natural bone: lydite samples
lydite <- filter(imp_data, Raw.material == "lydite")
for (i in num.var){
# get the min/max range of the data set
range_var <- range(lydite[i])
# plot bone plate
l_bp <- ggplot(lydite[grep("bone plate", lydite[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "lydite - bone plate", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
geom_line(aes(group = Spot,), colour = "#FAD510") +
scale_colour_manual(values = "#FAD510") +
theme_classic()
# plot cow scapula
l_cs <- ggplot(lydite[grep("cow scapula", lydite[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "lydite - cow scapula", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
scale_colour_manual(values = "#CB2314") +
geom_line(aes(group = Spot,), colour = "#CB2314") +
theme_classic()
# combine the lydite plots
l.bone <- l_bp + l_cs + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(l.bone)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_l.bone_",
names(imp_data)[i], ".pdf")
ggsave(filename = file_out, plot = l.bone, path = dir_out, device = "pdf")
}
Warning: Removed 1 rows containing missing values (geom_point).Warning: Removed 1 row(s) containing missing values (geom_path).Warning: Removed 1 rows containing missing values (geom_point).Warning: Removed 1 row(s) containing missing values (geom_path).
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Warning: Removed 1 rows containing missing values (geom_point).Warning: Removed 1 rows containing missing values (geom_point).
6.1.3 artificial vs. natural skin: flint samples
for (i in num.var){
# get the min/max range of the data set
range_var <- range(flint[i])
# plot skin pad
f_sp <- ggplot(flint[grep("skin pad", flint[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "flint - skin pad", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
geom_line(aes(group = Spot,), colour = "#046C9A") +
scale_colour_manual(values = "#046C9A") +
theme_classic()
# plot pig skin
f_ps <- ggplot(flint[grep("pig skin", flint[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "flint - pig skin", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
scale_colour_manual(values = "#52854c") +
geom_line(aes(group = Spot,), colour = "#52854c") +
theme_classic()
# combine the flint and the lydite plots
f.skin <- f_sp + f_ps + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(f.skin)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_f.skin_",
names(imp_data)[i], ".pdf")
ggsave(filename = file_out, plot = f.skin, path = dir_out, device = "pdf")
}
Warning: Removed 2 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 4 rows containing missing values (geom_point).Warning: Removed 3 row(s) containing missing values (geom_path).Warning: Removed 2 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 4 rows containing missing values (geom_point).Warning: Removed 3 row(s) containing missing values (geom_path).
Warning: Removed 2 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 4 rows containing missing values (geom_point).Warning: Removed 3 row(s) containing missing values (geom_path).Warning: Removed 2 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 4 rows containing missing values (geom_point).Warning: Removed 3 row(s) containing missing values (geom_path).
Warning: Removed 3 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).Warning: Removed 3 rows containing missing values (geom_point).Warning: Removed 2 row(s) containing missing values (geom_path).
6.1.4 artificial vs. natural skin: lydite samples
for (i in num.var){
# get the min/max range of the data set
range_var <- range(lydite[i])
# plot skin pad
l_sp <- ggplot(lydite[grep("skin pad", lydite[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "lydite - skin pad", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
geom_line(aes(group = Spot,), colour = "#046C9A") +
scale_colour_manual(values = "#046C9A") +
theme_classic()
# plot pig skin
l_ps <- ggplot(lydite[grep("pig skin", lydite[["Contact.material"]]), ],
aes_string(y = names(imp_data)[i], x = "Cycle")) +
geom_point(aes(shape = Spot, colour = Sample), size = 3) +
labs(title = "lydite - pig skin", y = names(imp_data)[i], x = "cycle") +
coord_cartesian(ylim = range_var) +
guides(colour = "none") +
scale_colour_manual(values = "#52854c") +
geom_line(aes(group = Spot,), colour = "#52854c") +
theme_classic()
# combine the flint and the lydite plots
l.skin <- l_sp + l_ps + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(l.skin)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_l.skin_", names(imp_data)[i],
".pdf")
ggsave(filename = file_out, plot = l.skin, path = dir_out, device = "pdf")
}
7 Plot the mean value (3 measurements) of each of the selected numeric variable
7.1 Sorted by contact material
7.1.1 cow scapula & bone plate: Sq
# remove possible outliers (based on values and prior plots)
imp_data2 <- imp_data[-c(1, 4, 7, 10, 13, 18, 32, 47, 75), ]
# add a column that combines sample and cycle
ID.cycle <- unite(imp_data2, ID_Cycle, c(Sample, Cycle), remove = FALSE)
# compute the mean of the three spots per sample
mean_cycle <- summaryBy(.~ ID_Cycle + Sample + Stroke + Raw.material + Contact.material,
data = ID.cycle, FUN = mean)
# define new num.var for mean_cycle
num.var2 <- 6:length(mean_cycle)
for (j in num.var2) cat("[",j,"] ", names(mean_cycle)[j], "\n", sep="")[6] Analysis.time.mean
[7] Sq.mean
[8] Ssk.mean
[9] Sku.mean
[10] Sp.mean
[11] Sv.mean
[12] Sz.mean
[13] Sa.mean
[14] Smr.mean
[15] Smc.mean
[16] Sxp.mean
[17] Sal.mean
[18] Str.mean
[19] Std.mean
[20] Sdq.mean
[21] Sdr.mean
[22] Vm.mean
[23] Vv.mean
[24] Vmp.mean
[25] Vmc.mean
[26] Vvc.mean
[27] Vvv.mean
[28] Maximum.depth.of.furrows.mean
[29] Mean.depth.of.furrows.mean
[30] Mean.density.of.furrows.mean
[31] Isotropy.mean
[32] First.direction.mean
[33] Second.direction.mean
[34] Third.direction.mean
[35] Texture.isotropy.mean
[36] epLsar.mean
[37] NewEplsar.mean
[38] Asfc.mean
[39] Smfc.mean
[40] HAsfc9.mean
[41] HAsfc81.mean # plot bone plate
Sq.bp <- ggplot(mean_cycle[grep("bone plate", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Sq.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "bone plate", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0, 7000)) +
scale_colour_manual(values = c("#FAD50F", "#FAD50F")) +
theme_classic()
# plot cow scapula
Sq.cs <- ggplot(mean_cycle[grep("cow scapula", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Sq.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0, 7000)) +
labs(title = "cow scapula", y = "ΔSq [nm]", x = "cycle") +
scale_colour_manual(values = c("#CB2213", "#CB2213")) +
theme_classic()
# combine the plots
Sq.bone.mean <- Sq.cs + Sq.bp + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(Sq.bone.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_Sq.bone.mean", ".pdf")
ggsave(filename = file_out, plot = Sq.bone.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.2 pig skin & skin pad: Sq
Sq.sp <- ggplot(mean_cycle[grep("skin pad", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Sq.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "skin pad", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0, 6000)) +
scale_colour_manual(values = c("#52854B", "#52854B")) +
theme_classic()
Sq.ps <- ggplot(mean_cycle[grep("pig skin", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Sq.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0, 6000)) +
labs(title = "pig skin", y = "ΔSq [nm]", x = "cycle") +
scale_colour_manual(values = c("#036C9A", "#036C9A")) +
theme_classic()
# combine the plots
Sq.skin.mean <- Sq.ps + Sq.sp + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(Sq.skin.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_Sq.skin.mean", ".pdf")
ggsave(filename = file_out, plot = Sq.skin.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.3 cow scapula & bone plate: Vmc
Vmc.bp <- ggplot(mean_cycle[grep("bone plate", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Vmc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "bone plate", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0, 3.5)) +
scale_colour_manual(values = c("#FAD50F", "#FAD50F")) +
theme_classic()
Vmc.cs <- ggplot(mean_cycle[grep("cow scapula", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Vmc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0, 3.5)) +
labs(title = "cow scapula", y = "Vmc [µm³/µm²]", x = "cycle") +
scale_colour_manual(values = c("#CB2213", "#CB2213")) +
theme_classic()
# combine the plots
Vmc.bone.mean <- Vmc.cs + Vmc.bp + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(Vmc.bone.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_Vmc.bone.mean", ".pdf")
ggsave(filename = file_out, plot = Vmc.bone.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.4 pig skin & skin pad: Vmc
Vmc.sp <- ggplot(mean_cycle[grep("skin pad", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Vmc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "skin pad", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0, 3.5)) +
scale_colour_manual(values = c("#52854B", "#52854B")) +
theme_classic()
Vmc.ps <- ggplot(mean_cycle[grep("pig skin", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Vmc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0, 3.5)) +
labs(title = "pig skin", y = "Vmc [µm³/µm²]", x = "cycle") +
scale_colour_manual(values = c("#036C9A", "#036C9A")) +
theme_classic()
# combine the plots
Vmc.skin.mean <- Vmc.ps + Vmc.sp + plot_layout(width = c(3/6, 3/6), guides = 'collect')
print(Vmc.skin.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_Vmc.skin.mean", ".pdf")
ggsave(filename = file_out, plot = Vmc.skin.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.5 cow scapula & bone plate: HAsfc9
HAsfc9.bp <- ggplot(mean_cycle[grep("bone plate", mean_cycle[["Contact.material"]]), ],
aes_string(y = "HAsfc9.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "bone plate", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0.0, 11.0)) +
scale_colour_manual(values = c("#FAD50F", "#FAD50F")) +
theme_classic()
HAsfc9.cs <- ggplot(mean_cycle[grep("cow scapula", mean_cycle[["Contact.material"]]), ],
aes_string(y = "HAsfc9.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0.0, 11.0)) +
labs(title = "cow scapula", y = "HAsfc9", x = "cycle") +
scale_colour_manual(values = c("#CB2213", "#CB2213")) +
theme_classic()
# combine the plots
HAsfc9.bone.mean <- HAsfc9.cs + HAsfc9.bp + plot_layout(width = c(3/6, 3/6),
guides = 'collect')
print(HAsfc9.bone.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_HAsfc9.bone.mean", ".pdf")
ggsave(filename = file_out, plot = HAsfc9.bone.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.6 pig skin & skin pad: HAsfc9
HAsfc9.sp <- ggplot(mean_cycle[grep("skin pad", mean_cycle[["Contact.material"]]), ],
aes_string(y = "HAsfc9.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "skin pad", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0.0, 11.0)) +
scale_colour_manual(values = c("#52854B", "#52854B")) +
theme_classic()
HAsfc9.ps <- ggplot(mean_cycle[grep("pig skin", mean_cycle[["Contact.material"]]), ],
aes_string(y = "HAsfc9.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0.0, 11.0)) +
labs(title = "pig skin", y = "HAsfc9", x = "cycle") +
scale_colour_manual(values = c("#036C9A", "#036C9A")) +
theme_classic()
# combine the plots
HAsfc9.skin.mean <- HAsfc9.ps + HAsfc9.sp + plot_layout(width = c(3/6, 3/6), guides =
'collect')
print(HAsfc9.skin.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_HAsfc9.skin.mean", ".pdf")
ggsave(filename = file_out, plot = HAsfc9.skin.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.7 cow scapula & bone plate: epLsar
epLsar.bp <- ggplot(mean_cycle[grep("bone plate", mean_cycle[["Contact.material"]]), ],
aes_string(y = "epLsar.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "bone plate", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0.000, 0.005)) +
scale_colour_manual(values = c("#FAD50F", "#FAD50F")) +
theme_classic()
epLsar.cs <- ggplot(mean_cycle[grep("cow scapula", mean_cycle[["Contact.material"]]), ],
aes_string(y = "epLsar.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0.000, 0.005)) +
labs(title = "cow scapula", y = "epLsar", x = "cycle") +
scale_colour_manual(values = c("#CB2213", "#CB2213")) +
theme_classic()
# combine the plots
epLsar.bone.mean <- epLsar.cs + epLsar.bp + plot_layout(width = c(3/6, 3/6),
guides = 'collect')
print(epLsar.bone.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_epLsar.bone.mean", ".pdf")
ggsave(filename = file_out, plot = epLsar.bone.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.8 pig skin & skin pad: epLsar
epLsar.sp <- ggplot(mean_cycle[grep("skin pad", mean_cycle[["Contact.material"]]), ],
aes_string(y = "epLsar.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "skin pad", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0.000, 0.005)) +
scale_colour_manual(values = c("#52854B", "#52854B")) +
theme_classic()
epLsar.ps <- ggplot(mean_cycle[grep("pig skin", mean_cycle[["Contact.material"]]), ],
aes_string(y = "epLsar.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0.000, 0.005)) +
labs(title = "pig skin", y = "epLsar", x = "cycle") +
scale_colour_manual(values = c("#036C9A", "#036C9A")) +
theme_classic()
# combine the plots
epLsar.skin.mean <- epLsar.ps + epLsar.sp + plot_layout(width = c(3/6, 3/6),
guides = 'collect')
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_epLsar.skin.mean", ".pdf")
ggsave(filename = file_out, plot = epLsar.skin.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.9 cow scapula & bone plate: Asfc
Asfc.bp <- ggplot(mean_cycle[grep("bone plate", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Asfc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "bone plate", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0, 35)) +
scale_colour_manual(values = c("#FAD50F", "#FAD50F")) +
theme_classic()
Asfc.cs <- ggplot(mean_cycle[grep("cow scapula", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Asfc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0, 35)) +
labs(title = "cow scapula", y = "Asfc", x = "cycle") +
scale_colour_manual(values = c("#CB2213", "#CB2213")) +
theme_classic()
# combine the plots
Asfc.bone.mean <- Asfc.cs + Asfc.bp + plot_layout(width = c(3/6, 3/6),
guides = 'collect')
print(Asfc.bone.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_Asfc.bone.mean", ".pdf")
ggsave(filename = file_out, plot = Asfc.bone.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")7.1.10 cow scapula & bone plate: Asfc
Asfc.sp <- ggplot(mean_cycle[grep("skin pad", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Asfc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
labs(title = "skin pad", y = NULL, x = "cycle") +
coord_cartesian(ylim = c(0, 35)) +
scale_colour_manual(values = c("#52854B", "#52854B")) +
theme_classic()
Asfc.ps <- ggplot(mean_cycle[grep("pig skin", mean_cycle[["Contact.material"]]), ],
aes_string(y = "Asfc.mean", x = "Stroke")) +
geom_point(aes(colour = Sample), size = 3) +
geom_line(aes(group = Sample)) +
scale_x_continuous(breaks = c(0, 50, 250, 1000, 2000)) +
coord_cartesian(ylim = c(0, 35)) +
labs(title = "pig skin", y = "Asfc", x = "cycle") +
scale_colour_manual(values = c("#036C9A", "#036C9A")) +
theme_classic()
# combine the plots
Asfc.skin.mean <- Asfc.ps + Asfc.sp + plot_layout(width = c(3/6, 3/6),
guides = 'collect')
print(Asfc.skin.mean)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_Asfc.skin.mean", ".pdf")
ggsave(filename = file_out, plot = Asfc.skin.mean, path = dir_out, device = "pdf",
width = 350, height = 270, units = "mm")8 sessionInfo() and RStudio version
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
attached base packages:
[1] tools stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggsci_2.9 patchwork_1.1.1 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[9] tibble_3.1.6 tidyverse_1.3.1 doBy_4.6.13 ggplot2_3.3.6
[13] R.utils_2.11.0 R.oo_1.24.0 R.methodsS3_1.8.1
loaded via a namespace (and not attached):
[1] lubridate_1.8.0 lattice_0.20-44 assertthat_0.2.1
[4] digest_0.6.29 utf8_1.2.2 R6_2.5.1
[7] cellranger_1.1.0 backports_1.4.1 reprex_2.0.1
[10] evaluate_0.15 highr_0.9 httr_1.4.2
[13] pillar_1.7.0 rlang_1.0.2 readxl_1.4.0
[16] rstudioapi_0.13 jquerylib_0.1.4 Matrix_1.3-3
[19] rmarkdown_2.14 labeling_0.4.2 munsell_0.5.0
[22] broom_0.8.0 compiler_4.1.0 Deriv_4.1.3
[25] modelr_0.1.8 xfun_0.30 pkgconfig_2.0.3
[28] microbenchmark_1.4.9 htmltools_0.5.2 tidyselect_1.1.2
[31] fansi_1.0.3 crayon_1.5.1 tzdb_0.3.0
[34] dbplyr_2.1.1 withr_2.5.0 MASS_7.3-54
[37] grid_4.1.0 jsonlite_1.8.0 gtable_0.3.0
[40] lifecycle_1.0.1 DBI_1.1.2 magrittr_2.0.3
[43] scales_1.2.0 cli_3.3.0 stringi_1.7.6
[46] farver_2.1.0 fs_1.5.2 xml2_1.3.3
[49] bslib_0.3.1 ellipsis_0.3.2 generics_0.1.2
[52] vctrs_0.4.1 glue_1.6.2 hms_1.1.1
[55] fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
[58] rvest_1.0.2 knitr_1.39 haven_2.5.0
[61] sass_0.4.1 RStudio version 1.4.1717.
9 Cite R packages used
for (i in pack_to_load) print(citation(i), bibtex = FALSE)
To cite package 'R.utils' in publications use:
Henrik Bengtsson (2021). R.utils: Various Programming Utilities. R
package version 2.11.0. https://CRAN.R-project.org/package=R.utils
The 'tools' package is part of R. To cite R in publications use:
R Core Team (2021). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
URL https://www.R-project.org/.
We have invested a lot of time and effort in creating R, please cite it
when using it for data analysis. See also 'citation("pkgname")' for
citing R packages.
To cite ggplot2 in publications, please use:
H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York, 2016.
To cite package 'doBy' in publications use:
Søren Højsgaard and Ulrich Halekoh (2022). doBy: Groupwise
Statistics, LSmeans, Linear Estimates, Utilities. R package version
4.6.13. https://CRAN.R-project.org/package=doBy
ATTENTION: This citation information has been auto-generated from the
package DESCRIPTION file and may need manual editing, see
'help("citation")'.
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open
Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
To cite package 'patchwork' in publications use:
Thomas Lin Pedersen (2020). patchwork: The Composer of Plots. R
package version 1.1.1. https://CRAN.R-project.org/package=patchwork
To cite package 'ggsci' in publications use:
Nan Xiao (2018). ggsci: Scientific Journal and Sci-Fi Themed Color
Palettes for 'ggplot2'. R package version 2.9.
https://CRAN.R-project.org/package=ggsciEND OF SCRIPT